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zenodo44/100

Supplementary dataset to the publication "Bi, S., and Hieronymi, M. (2024). Holistic optical water type classification for ocean, coastal, and inland waters. Limnology & Oceanography"

The NetCDF data files contain the training dataset used to develop the Optical Water Type (OWT) framework proposed by Bi and Hieronymi (2024). The dataset is available in two spectral versions:     1.    <code>owt_BH2024_training_data_hyper.nc</code>: This file includes training data with a spectral resolution of 2 nm, ranging from 400 to 900 nm.     2.    <code>owt_BH2024_training_data_olci.nc</code>: This file contains data formatted similarly to the hyperspectral version but aligned with the nominal Sentinel-3 OLCI wavebands. <h2>Contents of the Dataset</h2> For each version, the dataset includes spectral inherent and apparent optical properties such as:     •    Remote Sensing Reflectance (Rrs)     •    Pure Water Absorption (aw)     •    Absorption Coefficient of Detritus (ad)     •    Total Absorption Coefficient without Pure Water (agp)     •    Absorption Coefficient of Phytoplankton (aph)     •    Backscattering Coefficient of Total Particulate Matter (bbp)     •    Scattering Coefficient of Total Particulate Matter (bp)     •    Scattering Coefficient of Pure Water (bw) Additionally, the dataset includes various environmental and biological parameters:     •    Chlorophyll a Concentration (Chl)     •    Inorganic Suspended Matter Concentration (ISM)     •    Colored Dissolved Organic Matter Absorption at 440 nm (ag440)     •    Single-Scattering Albedo of Detritus at 550 nm (A_d)     •    Power Law Exponent of Detritus Attenuation (G_d)     •    Water Salinity (Sal)     •    Water Temperature (Temp)     •    Fraction for Diminished Coccolithophore Absorption (a_frac)     •    Fraction of Coccolithophore Group (cocco_frac) <h2>Optical Water Types</h2> The training dataset includes 10 pre-defined optical water types, with 10,000 samples for each type. Detailed descriptions of these water types can be found in Table 1 of Bi and Hieronymi (2024) or as follows, <table> <tbody> <tr> <td>OWT</td> <td>Desciption</td> </tr> <tr> <td>1</td> <td>Extremely clear and oligotrophic indigo-blue waters with high reflectance in the short visible wavelengths.</td> </tr> <tr> <td>2</td> <td>Blue waters with similar biomass level as OWT 1 but with slightly higher detritus and CDOM content.</td> </tr> <tr> <td>3a</td> <td>Turquoise waters with slightly higher phytoplankton, detritus, and CDOM compared to the first two types.</td> </tr> <tr> <td>3b</td> <td>A special case of OWT 3a with similar detritus and CDOM distribution but with strong scattering and little absorbing particles like in the case of Coccolithophore blooms. This type usually appears brighter and exhibits a remarkable ~490 nm reflectance peak.</td> </tr> <tr> <td>4a</td> <td>Greenish water found in coastal and inland environments, with higher biomass compared to the previous water types. Reflectance in short wavelengths is usually depressed by the absorption of particles and CDOM.</td> </tr> <tr> <td>4b</td> <td>A special case of OWT 4a, sharing similar detritus and CDOM distribution, exhibiting phytoplankton blooms with higher scattering coefficients, e.g., Coccolithophore bloom. The color of this type shows a very bright green.</td> </tr> <tr> <td>5a</td> <td>Green eutrophic water, with significantly higher phytoplankton biomass, exhibiting a bimodal reflectance shape with typical peaks at ~560 and ~709 nm.</td> </tr> <tr> <td>5b</td> <td>Green hyper-eutrophic water, with even higher biomass than that of OWT 5a (over several orders of magnitude), displaying a reflectance plateau in the Near Infrared Region, NIR (vegetation-like spectrum).</td> </tr> <tr> <td>6</td> <td>Bright brown water with high detritus concentrations, which has a high reflectance determined by scattering.</td> </tr> <tr> <td>7</td> <td>Dark brown to black water with very high CDOM concentration, which has low reflectance in the entire visible range and is dominated by absorption.</td> </tr> </tbody> </table> <h2>Additional Information</h2> The detailed description of the data simulation can be found in the supporting information of Bi and Hieronymi (2024). The models used for simulating the data are available on GitHub:     •    Component IOP Model: <a href="https://github.com/bishun945/IOPmodel" target="_blank" rel="noopener">Bio-geo-optical modelling of natural waters by Bi, Hieronymi, and Röttgers (2023)</a>     •    OWT Package: <a href="https://github.com/bishun945/pyOWT" target="_blank" rel="noopener">pyOWT</a> <h2>References</h2>     1.    OWT Framework: Bi, S., and Hieronymi, M. (2024). Holistic optical water type classification for ocean, coastal, and inland waters. Limnology & Oceanography, lno.12606. doi: 10.1002/lno.12606     2.    Component IOP Model: Bi, S., Hieronymi, M., and Röttgers, R. (2023). Bio-geo-optical modelling of natural waters. Front. Mar. Sci. 10, 1196352. doi: 10.3389/fmars.2023.1196352     3.    Pure Water IOP Model: Röttgers, R., Doerffer, R., McKee, D., and Schönfeld, W. (2016). The Water Optical Properties Processor (WOPP): Pure Water Spectral Absorption, Scattering and Real Part of Refractive Index Model. Technical Report No WOPP-ATBD/WRD6. Available at: https://calvalportal.ceos.org/tools     4.    Rrs Model: Lee, Z., Du, K., Voss, K. J., Zibordi, G., Lubac, B., Arnone, R., et al. (2011). An inherent-optical-property-centered approach to correct the angular effects in water-leaving radiance. Appl. Opt. 50, 3155. doi: 10.1364/AO.50.003155 <h2>Authors and Contact</h2>     •    Author: Shun Bi, Martin Hieronymi, Rüdiger Röttgers     •    Creator: Shun Bi, Shun.Bi@hereon.de <h2>Example Python Code to Read Data</h2> Here is an example of how to read the NetCDF data using Python and the <code>xarray</code> library: <pre><code>import xarray as xr # Load the dataset data_hyper = xr.open_dataset("path_to_your_file/owt_BH2024_training_data_hyper.nc") # Print the dataset to see its structure print(data_hyper) # Access a specific variable, e.g., remote sensing reflectance (Rrs) rrs = data_hyper['Rrs'] # Plot a sample of Rrs import matplotlib.pyplot as plt # Select a sample ID, for example the first sample sample_id = 0 plt.plot(data_hyper['wavelen'], rrs[sample_id, :]) plt.xlabel('Wavelength (nm)') plt.ylabel('Rrs (1/sr)') plt.title(f'Remote Sensing Reflectance for Sample ID {sample_id}') plt.show()</code></pre>

opencc-by-4.0Jul 2024View details →

zenodo44/100

Series data for the 22PN nonlinear memory effect in the l=2, m=0 mode.

Dataset associated with the preprint arXiv:2407.19017, “Waveform models for the gravitational-wave memory effect: Extreme mass-ratio limit and final memory offset” by Arwa Elhashash and David A. Nichols. It contains the 22 post-Newtonian-order series data for the l=2, m=0 spin-weighted spherical harmonic mode of the gravitational-wave memory signal from an extreme-mass ratio inspiral with nonspinning black holes.

opencc-by-4.0Jul 2024View details →

zenodo44/100

Dataset used to perform Focus Groups in Spain, Israel and Hungary (related to m-RESIST project)

Dataset used to perform the following manuscripts:  - Huerta-Ramos, E., Escobar-Villegas, M. S., Rubinstein, K., Unoka, Z. S., Grasa, E., Hospedales, M., … Usall, J. (2016). Measuring Users’ Receptivity Toward an Integral Intervention Model Based on mHealth Solutions for Patients With Treatment-Resistant Schizophrenia (m-RESIST): A Qualitative Study. JMIR mHealth and uHealth, 4(3), e112. http://doi.org/10.2196/mhealth.5716 rom March to June (2015), it was included opinions of patients, informal carers, and clinicians from the three countries concerning the services originally intended to be part of the solution. The activities related to the publication were the following: 9 focus groups (72 people) and 35 individual interviews were carried out in the 3 countries. All recorded data was analysed using discourse analysis as the framework.

opencc-by-4.0Sep 2016View details →

zenodo44/100

Map of Co-Seismic Landslides for the M 7.8 Kaikoura, New Zealand Earthquake

Prepared by the Research Group on Earthquake Geology in Greece (http://eqgeogr.weebly.com/) Version 2 (updated) With the release of new Sentinel-2 images, and other available resources for the M7.8 Kaikoura earthquake, we present an update of the Map of Co-Seismic Landslides and Surfaces Ruptures (As of 27/11/2016). Landslides were mapped using Sentinel-2 satellite images from Copernicus, European Space Agency, dated November and December 2016. Images were visually compared with previous last available S2A images without cloud cover (13 September and 26 October) and landslides and large slope failures were manually mapped. Areas covered by cloud are omitted and shown on map. 5875 landslide sites are shown in the map. A small number of landslides could have been mis-identified due to insufficient resolution of the images, small gaps of cloud cover or for other reasons. Also, re-activated landslides on the central mountainous area were unabled to identify due to imagery restrictions (medium resolution, relief shadows etc). Some local gaps in Sentinel imagery still exist due to cloud cover, but we believe the current map is very close to the major distribution of mass movement effects. Surface ruptures were mapped using Sentinel-2 imagery and approximate position from photos of the post-earthquake aerial surveys of Environment Canterbury Regional Council (http://ecan.govt.nz) KML file contains7355 landslide spots.

opencc-by-4.0Dec 2016View details →

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Herbarium specimen image of Papaver roseolum M. V. Agab. & Fragman, part of the collection of Botanic Garden and Botanical Museum Berlin

Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published. Content of this deposition: - A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values. - A JPEG image file of the scanned herbarium sheet. - A lossless TIFF image from which the JPEG image has been derived.

opencc-zeroNov 2018View details →

zenodo44/100

Coarse fragments % (volumetric) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution

Coarse fragments % (volumetric) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Based on machine learning predictions from global compilation of soil profiles and samples. Processing steps are described in detail <a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a>. Antarctica is not included. To access and visualize maps use:  <a href="http://www.openlandmap.org/">OpenLandMap.org</a> If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: <ul> <li>sol = theme: soil,</li> <li>coarsefrag.vfraction = variable: coarse fragments volumetric fraction,</li> <li>usda.3b1 = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>

opencc-by-nc-sa-4.0Dec 2018View details →

zenodo44/100

Silt content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution

Silt content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Based on machine learning predictions from global compilation of soil profiles and samples. Processing steps are described in detail <a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a>. Antarctica is not included. To access and visualize maps use:  <a href="http://www.openlandmap.org/">OpenLandMap.org</a> If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul>   All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: <ul> <li>sol = theme: soil,</li> <li>silt.wfraction = variable: silt weight fraction,</li> <li>usda.3a1a1a = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>

opencc-by-nc-sa-4.0Dec 2018View details →

zenodo44/100

Dataset for the paper: "Di Felice, L.J.; Ripa, M.; Giampietro, M. Deep Decarbonisation from a Biophysical Perspective: GHG Emissions of a Renewable Electricity Transformation in the EU."

Dataset used for the development of scenarios in the publication "Di Felice, L.J.; Ripa, M.; Giampietro, M. Deep Decarbonisation from a Biophysical Perspective: GHG Emissions of a Renewable Electricity Transformation in the EU. Sustainability 2018, 10, 3685." and used for a case study in "Di Felice L., Dunlop T., Giampietro M., Kovacic Z., Renner A., Ripa M., Velasco-Fernández R. – Report on the Quality Check of the Robustness of the Narrative behind Energy Directives. MAGIC (H2020–GA 689669) Project Deliverable 5.4, 30 November 2018". (link: https://magic-nexus.eu/documents/d54-report-narratives-behind-energy-directives). Sources of other secondary data (from papers, reports) specified in the dataset (under tab "input codes")

opencc-by-4.0Jan 2019View details →

zenodo44/100

Soil available water capacity in mm derived for 5 standard layers (0-10, 10-30, 30-60, 60-100 and 100-200 cm) at 250 m resolution

Available Water Capacity (in mm) derived by calculating Water Retention Difference (difference between the field capacity and wilting point; see <a href="https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/ref/?cid=nrcs142p2_054247">NRCS Soil Survey Laboratory Methods Manual</a>), and then summing up WRD for all standard layers (0–200 cm). Soil water content (volumetric) in percent for 33 kPa and 1500 kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution is available <a href="https://doi.org/10.5281/zenodo.2609113">here</a>. These estimates ignore depth to bedrock i.e. existence of any impenetrable layer (total available capacity over the whole land mass is likely about 10–15% smaller). Antarctica is not included. To access and visualize some of the maps use:  <a href="http://www.openlandmap.org/">OpenLandMap.org</a> If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a></li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: <ul> <li>sol = theme: soil,</li> <li>available.water.capacity = available water capacity in mm,</li> <li>usda.mm = determination method: Water Retention Difference in mm,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..10cm = vertical reference: 0-10 cm layer below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.1 = version number: 0.1,</li> </ul>

opencc-by-nc-sa-4.0Apr 2019View details →

zenodo44/100

Exploring the age dependent properties of M and L dwarfs using Gaia and SDSS: The Sample

Sample from "Exploring the age dependent properties of M and L dwarfs using Gaia and SDSS". We present a sample of 74,216 M and L dwarfs constructed from two existing catalogs of cool dwarfs spectroscopically identified in the Sloan Digital Sky Survey (SDSS). We  cross-matched the SDSS catalog with Gaia DR2 to obtain parallaxes and proper motions and modified the quality cuts suggested by the Gaia Collaboration to make them suitable for late-M and L dwarfs.

opencc-by-4.0Apr 2019View details →

zenodo44/100

Data from: Andriollo T., Gillet F., Michaux J.R., Ruedi M. (2019). The menu varies with metabarcoding practices: A case study with the bat Plecotus auritus. PLoS ONE 14(7)

Supporting data for: Andriollo T., Gillet F., Michaux J.R., Ruedi M. (2019). The menu varies with metabarcoding practices: a case study with the bat Plecotus auritus. PLoS ONE 14(7): e0219135. https://doi.org/10.1371/journal.pone.0219135 Raw DNA sequences of prey of Plecotus auritus. Sampling information separated by semicolums as folows: >Sequence number; Colony; Date; Sample name; Dataset; Is the sequence attributable to the diet or not (Diet); Read numbers (Size); DNA sequence

opencc-by-4.0Jul 2019View details →

zenodo44/100

Global river density, seasonal and surface water occurrence and upstream area at 250 m in the Goode Homolosine projection

Several layers describing density of surface water / streams projected to the <a href="https://en.wikipedia.org/wiki/Goode_homolosine_projection">Good Homolosine projection</a>. List of layers included: <ul> <li>hyd_log1p.upstream.area_merit.hydro_m = Upstream Drainage Area based on the <a href="http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro">MERIT Hydro</a>,</li> <li>hyd_river.density_gloric_p = rasterized <a href="https://www.hydrosheds.org/page/gloric">Global River Classification (GLORIC)</a> DB,</li> <li>lcv_water.occurance_jrc.surfacewater_p = Surface Water based on the JRC's <a href="https://global-surface-water.appspot.com/">Global Surface Water</a>,</li> <li>lcv_water.seasonal_probav.glc.lc100_p = Seasonal Inland Water probability based on the <a href="https://lcviewer.vito.be/">Copernicus LC100 map</a>,</li> <li>lcv_wetlands.cw_upmc.wtd_c = composite wetland (CW) map based on <a href="https://doi.org/10.1594/PANGAEA.892657">Tootchi et al. (2019)</a>,</li> <li>Goode_Homolosine_domain_250m.tif = map domain prepared by <a href="https://doi.org/10.5281/zenodo.1475152">Luís de Sousa</a>,</li> <li>tiles_GH_100km_land.gpkg = 100 km x 100 km tiling system covering the land mass,</li> </ul> Important notes: Processing steps are described in detail <a href="https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/WaterDensity">here</a>. Antartica is not included. Reprojecting maps to Goode Homolosine projection can be cumbersome and small amount of artifacts at the edges of the map can be anticipated. These maps were develop in connection to the <a href="http://www.OpenLandMap.org">OpenLandMap.org</a> initiative. If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: <ul> <li>hyd = theme: hydrology and water dynamics,</li> <li>log1p.upstream.area = variable: log(X+1)*10 of the upstream area,</li> <li>merit.hydro = determination method: MERIT Hydro,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..0cm = vertical reference: surface,</li> <li>2017 = time reference: period 2017,</li> <li>v0.1 = version number: 0.1,</li> </ul>

opencc-by-nc-sa-4.0Jul 2019View details →

zenodo44/100

Databases with structures used for "Improving the Activity of M-N4 Catalysts for the Oxygen Reduction Reaction by Electrolyte Adsorption"

DFT optimised structures used for the paper "Improving the Activity of M-N4 Catalysts for the Oxygen Reduction Reaction by Electrolyte Adsorption". There is a separate database for structures with Cr, Mn, Fe and Co as the central metal atom in the M-N4 motif, and one with the molecular references. The structures can be retrieved using the Atomic Simulation Environment (ASE).

opencc-by-4.0Sep 2019View details →

zenodo44/100

MAR-M-247 creep assessment through a modified theta projection model - Figures 2 and 5

These two programs provide a way to rebuild the MAR-M-247 creep data presented in the paper: G. Maggiani, M.J. Roy, S. Colantoni, P.J. Withers, MAR-M-247 creep assessment through a modified theta projection model, Materialia, Volume 7, 2019, 100392, ISSN 2589-1529, https://doi.org/10.1016/j.mtla.2019.100392. http://www.sciencedirect.com/science/article/pii/S2589152919301887)   In Paper_Figure_2.m two coefficients of the paper itself are corrected and a comparison with what written in the paper and the corrected value is provided. One typo error for theta 1 at 982°C and 140 MPa where 6.9 must be 1.9. The other is for 1038°C 50 MPa theta4. In the paper it is written e^-11 while it actually should have been e^-10. Paper_Figure_5.m more decimal values are provided for the coefficients a, b, c and d that are used to rebuild the theta values.

opencc-by-4.0Nov 2019View details →

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Olalla-Tárraga, M. Á. and Rodríguez, M. Á, 2007: Olalla-Tárraga and Rodríguez, 2007

Olalla-Tárraga, M. Á. and Rodríguez, M. Á. (2007), Energy and interspecific body size patterns of amphibian faunas in Europe and North America: anurans follow Bergmann__s rule, urodeles its converse. Global Ecology and Biogeography, 16: 606–617. doi: 10.1111/j.1466-8238.2007.00309.xOlalla-Tárraga, M. Á. and Rodríguez, M. Á. (2007), Energy and interspecific body size patterns of amphibian faunas in Europe and North America: anurans follow Bergmann__s rule, urodeles its converse. Global Ecology and Biogeography, 16: 606–617. doi: 10.1111/j.1466-8238.2007.00309.x

opencc-by-4.0Aug 2024View details →

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Olalla-Tárraga, M. Á. and Rodríguez, M. Á, 2007: Olalla-Tárraga, M. Á. and Rodríguez, M. Á., 2007

opencc-by-4.0Aug 2024View details →

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Gummern - Mining Waste Deposits 0.305m DEM (2023-10-02) from Pleaiades Neo

<h2>Abstract</h2> Mining Waste Deposits 0.305m Digital Elevation Model derived from 2023-10-02 Panchromatic TriStereo Pleiades Neo Dataset. This depositry contains data generated within the European S34 project. <h2>Metadata Information</h2> <table> <tbody> <tr> <td> Identification </td> </tr> <tr> <td> Full Title </td> <td> Mining Waste Deposits 0.305m DEM (2023-10-02) from Pleaiades Neo </td> </tr> <tr> <td> Abstract </td> <td> Mining Waste Deposits 0.305m Digital Elevation Model derived from 2023-10-02 Panchromatic TriStereo Pleiades Neo Dataset </td> </tr> <tr> <td> Keywords </td> <td> DEM, DSM, DTM, Pleiades Neo, Minning Waste Deposits </td> </tr> <tr> <td> Pilot area </td> <td> Gummern </td> </tr> <tr> <td> Associated resources </td> <td> Compare with "Mining Waste Deposits 5.73cm DEM UAV-derived" and "Mining Waste Deposits 0.495m DEM (2024-04-12) from World-View2" </td> </tr> <tr> <td> Language </td> <td> English </td> </tr> <tr> <td> URL </td> <td> / </td> </tr> <tr> <td> Categories </td> <td> Elevation </td> </tr> <tr> <td> Temporal reference </td> </tr> <tr> <td> Creation date (dd.mm.yyyy) </td> <td> 20.06.2024 </td> </tr> <tr> <td> Revision date (dd.mm.yyyy) </td> <td> 20.06.2024 </td> </tr> <tr> <td> Quality and validity </td> </tr> <tr> <td> Representation type </td> <td> Raster </td> </tr> <tr> <td> Fromat </td> <td> GeoTIFF </td> </tr> <tr> <td> Lineage </td> <td> / </td> </tr> <tr> <td> Spatial resolution </td> <td> 0.30495m </td> </tr> <tr> <td> Positional accuracy </td> <td> 0.5m </td> </tr> <tr> <td> Maintenance information </td> <td> / </td> </tr> <tr> <td> Coordinate system </td> <td> EPSG 25833 </td> </tr> <tr> <td> Constranits related to access and use </td> </tr> <tr> <td> Use limitation </td> <td> / </td> </tr> <tr> <td> Access constraint </td> <td> / </td> </tr> <tr> <td> Public/Private </td> <td> Public </td> </tr> <tr> <td> Responsible organisation </td> </tr> <tr> <td> Responsible Contact </td> <td> Enoc Sanz Ablanedo (esana@unileon.es) </td> </tr> <tr> <td> Responsible Party </td> <td> UNILEON </td> </tr> <tr> <td> Metadata on metadata </td> </tr> <tr> <td> Contact </td> <td> Enoc Sanz Ablanedo (esana@unileon.es) </td> </tr> <tr> <td> Metadata language </td> <td> English </td> </tr> </tbody> </table>

opencc-by-4.0Aug 2024View details →

zenodo44/100

Gummern - Digital elevation model 0.5 m, created from Pléiades Neo tri-stereo satellite imagery

<h2>Abstract</h2> Digital surface model, spatial resolution 0.5 m, produced from Pleiades Neo Tri-Stereo imagery & Ground control points for Pleiades Neo tri-stereo orientation. This depositry contains data generated within the European S34 project. <h2>Metadata Information</h2> <table> <tbody> <tr> <td> Identification </td> </tr> <tr> <td> Full Title </td> <td> Gummern_PleiadesNeo_DSM05m & Gummern_PleiadesNeo_GCPs </td> </tr> <tr> <td> Abstract </td> <td> Digital surface model, spatial resolution 0.5 m, produced from Pleiades Neo Tri-Stereo imagery & Ground control points for Pleiades Neo tri-stereo orientation </td> </tr> <tr> <td> Keywords </td> <td> surface model, Pleiades Neo, tri-stereo </td> </tr> <tr> <td> Pilot area </td> <td> Gummern </td> </tr> <tr> <td> Associated resources </td> <td> Gummern_PleiadesNeo_GCPs </td> </tr> <tr> <td> Language </td> <td> English </td> </tr> <tr> <td> URL </td> <td> / </td> </tr> <tr> <td> Categories </td> <td> Elevation & GNSS </td> </tr> <tr> <td> Temporal reference </td> </tr> <tr> <td> Creation date (dd.mm.yyyy) </td> <td> 1.10.2023 (27.10.2023 – GNSS) </td> </tr> <tr> <td> Revision date (dd.mm.yyyy) </td> <td> 1.12.2023 (6.11.2023 – GNSS) </td> </tr> <tr> <td> Quality and validity </td> </tr> <tr> <td> Representation type </td> <td> Raster / Vector </td> </tr> <tr> <td> Fromat </td> <td> GeoTIFF / CSV </td> </tr> <tr> <td> Lineage </td> <td> / </td> </tr> <tr> <td> Spatial resolution </td> <td> 0.5m </td> </tr> <tr> <td> Positional accuracy </td> <td> 0.30m (0.01m – GNSS) </td> </tr> <tr> <td> Maintenance information </td> <td> / </td> </tr> <tr> <td> Coordinate system </td> <td> EPSG 25833 </td> </tr> <tr> <td> Constranits related to access and use </td> </tr> <tr> <td> Use limitation </td> <td> / </td> </tr> <tr> <td> Access constraint </td> <td> / </td> </tr> <tr> <td> Public/Private </td> <td> Public </td> </tr> <tr> <td> Responsible organisation </td> </tr> <tr> <td> Responsible Contact </td> <td> Dejan Grigillo (dejan.grigillo@fgg.uni-lj.si) </td> </tr> <tr> <td> Responsible Party </td> <td> UL </td> </tr> <tr> <td> Metadata on metadata </td> </tr> <tr> <td> Contact </td> <td> Dejan Grigillo (dejan.grigillo@fgg.uni-lj.si) </td> </tr> <tr> <td> Metadata language </td> <td> English </td> </tr> </tbody> </table>

opencc-by-4.0Aug 2024View details →

zenodo44/100

Gummern - Mining Waste Deposits 0.495m DEM (2024-04-12) from WorldView2

<h2>Abstract</h2> Mining Waste Deposits 0.495m resolution Digital Elevation Model derived from 2024-04-12 Panchromatic Stereo WorldView2 Dataset. This depositry contains data generated within the European S34 project. <h2>Metadata Information</h2> <table> <tbody> <tr> <td> Identification </td> </tr> <tr> <td> Full Title </td> <td> Mining Waste Deposits 0.495m DEM (2024-04-12) from WorldView2 </td> </tr> <tr> <td> Abstract </td> <td> Mining Waste Deposits 0.495m resolution Digital Elevation Model derived from 2024-04-12 Panchromatic Stereo WorldView2 Dataset </td> </tr> <tr> <td> Keywords </td> <td> DEM, DSM, DTM, Pleiades Neo, WorldView2, Minning Waste Deposits </td> </tr> <tr> <td> Pilot area </td> <td> Gummern </td> </tr> <tr> <td> Associated resources </td> <td> Compare with "Mining Waste Deposits 5.73cm DEM UAV-derived" and "Mining Waste Deposits 0.305m DEM (2023-10-02) from Pleaiades Neo" </td> </tr> <tr> <td> Language </td> <td> English </td> </tr> <tr> <td> URL </td> <td> / </td> </tr> <tr> <td> Categories </td> <td> Elevation </td> </tr> <tr> <td> Temporal reference </td> </tr> <tr> <td> Creation date (dd.mm.yyyy) </td> <td> 12.04.2024 </td> </tr> <tr> <td> Revision date (dd.mm.yyyy) </td> <td> 12.04.2024 </td> </tr> <tr> <td> Quality and validity </td> </tr> <tr> <td> Representation type </td> <td> Raster </td> </tr> <tr> <td> Fromat </td> <td> GeoTIFF </td> </tr> <tr> <td> Lineage </td> <td> / </td> </tr> <tr> <td> Spatial resolution </td> <td> 0.495m </td> </tr> <tr> <td> Positional accuracy </td> <td> 0.5m </td> </tr> <tr> <td> Maintenance information </td> <td> / </td> </tr> <tr> <td> Coordinate system </td> <td> EPSG 25833 </td> </tr> <tr> <td> Constranits related to access and use </td> </tr> <tr> <td> Use limitation </td> <td> / </td> </tr> <tr> <td> Access constraint </td> <td> / </td> </tr> <tr> <td> Public/Private </td> <td> Public </td> </tr> <tr> <td> Responsible organisation </td> </tr> <tr> <td> Responsible Contact </td> <td> Enoc Sanz Ablanedo (esana@unileon.es) </td> </tr> <tr> <td> Responsible Party </td> <td> UNILEON </td> </tr> <tr> <td> Metadata on metadata </td> </tr> <tr> <td> Contact </td> <td> Enoc Sanz Ablanedo (esana@unileon.es) </td> </tr> <tr> <td> Metadata language </td> <td> English </td> </tr> </tbody> </table>

opencc-by-4.0Aug 2024View details →

zenodo44/100

Data for publication "The ZiCOS-M CO2 sensor network: measurement performance and CO2 variability across Zürich"

Please see README.md for a description of this package.  This work was funded by the European Union's Horizon 2020 research and innovation programme, grant agreement number 101037319, named Pilot Applications in Urban Landscapes - towards integrated city observatories for greenhouse gases (PAUL) and is known as ICOS Cities.

opencc-by-4.0Sep 2024View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record